Resting-state EEG signatures of Alzheimer’s disease are driven by periodic but not aperiodic changes

Electroencephalography (EEG) has shown potential for identifying early-stage biomarkers of neurocognitive dysfunction associated with dementia due to Alzheimer’s disease (AD). A large body of evidence shows that, compared to healthy controls (HC), AD is associated with power increases in lower EEG frequencies (delta and theta) and decreases in higher frequencies (alpha and beta), together with slowing of the peak alpha frequency. However, the pathophysiological processes underlying these changes remain unclear. For instance, recent studies have shown that apparent shifts in EEG power from high to low frequencies can be driven either by frequency specific periodic power changes or rather by non-oscillatory (aperiodic) changes in the underlying 1/f slope of the power spectrum. Hence, to clarify the mechanism(s) underlying the EEG alterations associated with AD, it is necessary to account for both periodic and aperiodic characteristics of the EEG signal. Across two independent datasets, we examined whether resting-state EEG changes linked to AD reflect true oscillatory (periodic) changes, changes in the aperiodic (non-oscillatory) signal, or a combination of both. We found strong evidence that the alterations are purely periodic in nature, with decreases in oscillatory power at alpha and beta frequencies (AD < HC) leading to lower (alpha + beta) / (delta + theta) power ratios in AD. Aperiodic EEG features did not differ between AD and HC. By replicating the findings in two cohorts, we provide robust evidence for purely oscillatory pathophysiology in AD and against aperiodic EEG changes. We therefore clarify the alterations underlying the neural dynamics in AD and emphasise the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.


Supplementary Table S1. Participant demographics
Note: The table shows mean and standard deviation (M(SD)) for age, years of education, and MMSE scores for each cohort. Number of females and right-handed individuals is also reported. Age and years of education were compared using independent-samples t-test. Note that years of education in cohort two were only available for N = 37 (27 AD) individuals. MMSE scores were compared with a Kruskal-Wallis test, and gender and handedness were compared with Fisher's exact test.

Supplementary Section 1: The SPR captures EEG features beyond oscillatory power alterations
We examined the extent to which the exponent and offset of the aperiodic signal contribute to the original SPR with bivariate correlation analyses. Figure S1 plots the relationship between the aperiodic exponent and the original SPR within each diagnostic group separately. When the full samples (both AD and HC) were considered together, in each cohort separately, moderate negative relationships between original SPR and exponent were found, with r = -.488, p = .001 in Cohort 1 and r = -.621, p < .0001 in Cohort 2. This relationship was significant within the AD groups in both cohorts and within the HC group in cohort 1 (all p's < .05) but did not reach statistical significance in the HC group in cohort 2 (p > .05).
Figure S1 further plots the relationships between the aperiodic offset and the original SPR. When the full samples were considered, moderate negative relationships were found in both cohort 1 (r = -0.380, p = .010) and cohort 2 (r = -0.457, p .002). When each diagnostic group was examined separately, AD groups in both cohorts and the HC in cohort 1 showed significant offset-ratio relationships (p's < .05), while HC from cohort 2 did not (p > .05) ( Figure S2). Hence, taken together, these results confirm that aperiodic EEG features contribute to the previously calculated SPR.
Additionally, the relative contribution to SPR of periodic parameters describing the dominant peak within each individual spectrum was investigated. In both cohorts, correlations of peak parameters with original SPR showed that the captured oscillatory changes were primarily driven by alpha power differences and alpha center frequency, whilst bandwidth did not contribute significantly ( Figure S2).   112)). When the entire sample (AD and HC) was considered together, in Cohort 1, correlations of peak parameters with the non-corrected power ratio suggested the captured oscillatory changes were primarily driven by alpha power differences (r = .702, p < .0001) followed by alpha center frequency (r = .552, p < .0001), while bandwidth did not contribute significantly (r =.031, p = .842). This pattern was similar in the second cohort, with the strongest correlations found for peak alpha power r = .580 (<.0001) and center frequency r = .607 (<.0001), while bandwidth was not associated significantly with the original SPR r = .188 (.202).

Supplementary Section 2: Aperiodic knee model analysis
In some subjects we noticed 'knees' (i.e., bends) in the power spectra. We therefore ran an exploratory follow up analysis using the spectral parameterization model in the knee mode. The settings for model fitting were as follows: frequency range 3-40 Hz (0.1 Hz resolution), aperiodic mode ('knee'), peak width ([1-12]), maximum number of peaks (7) The 'knee' of neural power spectra differentiates between AD and HC Figure S3A shows the group averaged aperiodic component fitted with a knee parameter in Cohort 1. When between diagnostic group differences were considered ( Figure S3B) significant differences were found in the knee frequency (F(1,37) = 15.305, p < 0.001, , 2 = 0.293), as well as offset (F(1.37) = 11.374, p = 0.002, 2 = 0.235) and exponent (F(1,37) = 11.705, p = 0.002, 2 = 0.240). As shown in Figure   S3C, the knee frequency (i.e., the point at which the aperiodic fit transitions from horizontal to negatively sloped) correlated strongly with the original spectral power ratio (r = 0.807, p < 0.0001) in the 1 st cohort as a whole and this relationship was statistically significant within each diagnostic group as well (HC r = 0.707(<.0001), AD r = 0.881(<.0001)).
These results were replicated in Cohort 2. Figure S3D shows the group averaged aperiodic fit with the knee included in the model for Cohort 2. The knee frequency was significantly higher in healthy controls compared to AD (F(1,39) = 5.143, p = .034, 2 = 0.189) ( Figure S3E). In line with Cohort 1, both offset (F(1,39) = 8.718, p =0.007, 2 = 0.284) and exponent (F(1,39) = 10.770, p = 0.003, 2 = 0.329) showed significant between-group differences as well. As in Cohort 1, a strong positive correlation was found between the knee frequency and the original SPR (r = 0.815, p < 0.0001) which was also statistically significant when each diagnostic group was examined separately (AD: r = 0.793(< 0.0001), HC: 0.750(0.002) ( Figure S3F). Given the significant alterations in the knee frequency in both cohorts as well as the strong relationship between knee and SPR, we also examined the between-group differences in purely oscillatory EEG measures after controlling for aperiodic signal. Figures S4A&D plot the aperiodic removed spectra for each cohort respectively. As Figure S4B shows, the difference in spectral power ratio also survived the aperiodic correction in the knee analysis: F(1,37) = 4.217, p = 0.047, 2 = 0.114.

Figure S4:
Oscillatory EEG changes in 'knee' model analysis. A-C Cohort 1 results. A The group averaged spectra after the aperiodic activity has been subtracted from the raw spectra for each participant (AD: blue, HC: green). The shaded areas represent standard error. B Comparison of aperiodic-adjusted SPR (log-transformed) showed a significant between-group difference. C Between-group comparison of periodic parameters showed power at the dominant alpha (5-15 Hz) peak is significantly reduced in AD, bandwidth is increased, whilst centre frequency does not differ. D-F Cohort 2 results. D Group averaged periodic components of the power spectrum (AD: purple, HC: yellow). Shaded area represents standard error. E Aperiodic-adjusted SPR computed from periodic activity did not differ significantly. F No significant between-group differences were found in (5-15Hz) peak parameters. * p < .05, ns p > .05.
The relationship between knee frequency and each cognitive composite measure (dementia severity, learning & memory, and executive function) were also tested. However, when age was controlled for, knee frequency did not uniquely predict any of the cognitive functions in AD or HC groups across both cohorts (all p's > .05).
While human electrophysiological recordings often knees in power spectra (especially in larger frequency ranges) (Seymour et al., 2022), currently little is known about the neurophysiological significance of the knee frequency parameter in the EEG signal. In invasive intracranial recordings, the knee frequency has been linked to neuronal timescales, which scale with cognitive functions and aging (Gao et al., 2020). However, it is unclear whether the knee represents a meaningful feature of the aperiodic signal or merely captures periodic influence on the shape of the power spectra. Our results could be consistent with the latter, as we observed very strong correlations between the knee frequency and SPR in both cohorts (1 & 2) and subtracting the aperiodic component (fitted with a knee) attenuated the between group differences in periodic features. Nevertheless, given the limited understanding of the knee frequency in non-invasive EEG recordings, we cannot interpret our results with confidence.

Table S2
Results of partial correlations for the unique effects of periodic parameters on neurocognitive functions while controlling for age Note: We were unable to analyse relationships to neuropsychological function in HC group in cohort 2 due to low numbers of participants who underwent cognitive testing.